2,791 research outputs found
Practical Datatype Specializations with Phantom Types and Recursion Schemes
Datatype specialization is a form of subtyping that captures program
invariants on data structures that are expressed using the convenient and
intuitive datatype notation. Of particular interest are structural invariants
such as well-formedness. We investigate the use of phantom types for describing
datatype specializations. We show that it is possible to express
statically-checked specializations within the type system of Standard ML. We
also show that this can be done in a way that does not lose useful programming
facilities such as pattern matching in case expressions.Comment: 25 pages. Appeared in the Proc. of the 2005 ACM SIGPLAN Workshop on
M
A Quantum Computational Learning Algorithm
An interesting classical result due to Jackson allows polynomial-time
learning of the function class DNF using membership queries. Since in most
practical learning situations access to a membership oracle is unrealistic,
this paper explores the possibility that quantum computation might allow a
learning algorithm for DNF that relies only on example queries. A natural
extension of Fourier-based learning into the quantum domain is presented. The
algorithm requires only an example oracle, and it runs in O(sqrt(2^n)) time, a
result that appears to be classically impossible. The algorithm is unique among
quantum algorithms in that it does not assume a priori knowledge of a function
and does not operate on a superposition that includes all possible states.Comment: This is a reworked and improved version of a paper originally
entitled "Quantum Harmonic Sieve: Learning DNF Using a Classical Example
Oracle
Total Domishold Graphs: a Generalization of Threshold Graphs, with Connections to Threshold Hypergraphs
A total dominating set in a graph is a set of vertices such that every vertex
of the graph has a neighbor in the set. We introduce and study graphs that
admit non-negative real weights associated to their vertices such that a set of
vertices is a total dominating set if and only if the sum of the corresponding
weights exceeds a certain threshold. We show that these graphs, which we call
total domishold graphs, form a non-hereditary class of graphs properly
containing the classes of threshold graphs and the complements of domishold
graphs, and are closely related to threshold Boolean functions and threshold
hypergraphs. We present a polynomial time recognition algorithm of total
domishold graphs, and characterize graphs in which the above property holds in
a hereditary sense. Our characterization is obtained by studying a new family
of hypergraphs, defined similarly as the Sperner hypergraphs, which may be of
independent interest.Comment: 19 pages, 1 figur
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